Overview

Dataset statistics

Number of variables26
Number of observations201
Missing cells51
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.0 KiB
Average record size in memory208.7 B

Variable types

Numeric16
Categorical10

Alerts

fuel-type is highly imbalanced (53.3%)Imbalance
engine-location is highly imbalanced (88.8%)Imbalance
num-of-cylinders is highly imbalanced (58.6%)Imbalance
normalized-losses has 37 (18.4%) missing valuesMissing
bore has 4 (2.0%) missing valuesMissing
stroke has 4 (2.0%) missing valuesMissing
symboling has 65 (32.3%) zerosZeros

Reproduction

Analysis started2023-12-27 07:32:49.941593
Analysis finished2023-12-27 07:34:27.106220
Duration1 minute and 37.16 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84079602
Minimum-2
Maximum3
Zeros65
Zeros (%)32.3%
Negative25
Negative (%)12.4%
Memory size1.7 KiB
2023-12-27T13:04:27.329237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2548017
Coefficient of variation (CV)1.4923973
Kurtosis-0.70717762
Mean0.84079602
Median Absolute Deviation (MAD)1
Skewness0.19737036
Sum169
Variance1.5745274
MonotonicityNot monotonic
2023-12-27T13:04:27.694480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 65
32.3%
1 52
25.9%
2 32
15.9%
3 27
13.4%
-1 22
 
10.9%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.9%
0 65
32.3%
1 52
25.9%
2 32
15.9%
3 27
13.4%
ValueCountFrequency (%)
3 27
13.4%
2 32
15.9%
1 52
25.9%
0 65
32.3%
-1 22
 
10.9%
-2 3
 
1.5%

normalized-losses
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)31.1%
Missing37
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean122
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:28.129299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile74
Q194
median115
Q3150
95-th percentile188
Maximum256
Range191
Interquartile range (IQR)56

Descriptive statistics

Standard deviation35.442168
Coefficient of variation (CV)0.29050957
Kurtosis0.52544039
Mean122
Median Absolute Deviation (MAD)24
Skewness0.76597642
Sum20008
Variance1256.1472
MonotonicityNot monotonic
2023-12-27T13:04:28.595971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161 11
 
5.5%
91 8
 
4.0%
150 7
 
3.5%
134 6
 
3.0%
128 6
 
3.0%
104 6
 
3.0%
85 5
 
2.5%
94 5
 
2.5%
65 5
 
2.5%
102 5
 
2.5%
Other values (41) 100
49.8%
(Missing) 37
 
18.4%
ValueCountFrequency (%)
65 5
2.5%
74 5
2.5%
77 1
 
0.5%
78 1
 
0.5%
81 2
 
1.0%
83 3
1.5%
85 5
2.5%
87 2
 
1.0%
89 2
 
1.0%
90 1
 
0.5%
ValueCountFrequency (%)
256 1
 
0.5%
231 1
 
0.5%
197 2
 
1.0%
194 2
 
1.0%
192 2
 
1.0%
188 2
 
1.0%
186 1
 
0.5%
168 5
2.5%
164 2
 
1.0%
161 11
5.5%

make
Categorical

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
108 

Length

Max length13
Median length11
Mean length6.5024876
Min length3

Characters and Unicode

Total characters1307
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.9%
nissan 18
 
9.0%
mazda 17
 
8.5%
mitsubishi 13
 
6.5%
honda 13
 
6.5%
volkswagen 12
 
6.0%
subaru 12
 
6.0%
peugot 11
 
5.5%
volvo 11
 
5.5%
dodge 9
 
4.5%
Other values (12) 53
26.4%

Length

2023-12-27T13:04:29.103535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.9%
nissan 18
 
9.0%
mazda 17
 
8.5%
mitsubishi 13
 
6.5%
honda 13
 
6.5%
volkswagen 12
 
6.0%
subaru 12
 
6.0%
peugot 11
 
5.5%
volvo 11
 
5.5%
dodge 9
 
4.5%
Other values (12) 53
26.4%

Most occurring characters

ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
n 71
 
5.4%
u 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1296
99.2%
Dash Punctuation 11
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 153
11.8%
o 151
 
11.7%
s 106
 
8.2%
t 100
 
7.7%
e 80
 
6.2%
n 71
 
5.5%
u 71
 
5.5%
i 65
 
5.0%
d 62
 
4.8%
m 57
 
4.4%
Other values (14) 380
29.3%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1296
99.2%
Common 11
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 153
11.8%
o 151
 
11.7%
s 106
 
8.2%
t 100
 
7.7%
e 80
 
6.2%
n 71
 
5.5%
u 71
 
5.5%
i 65
 
5.0%
d 62
 
4.8%
m 57
 
4.4%
Other values (14) 380
29.3%
Common
ValueCountFrequency (%)
- 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
n 71
 
5.4%
u 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

fuel-type
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
181 
diesel
20 

Length

Max length6
Median length3
Mean length3.2985075
Min length3

Characters and Unicode

Total characters663
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 181
90.0%
diesel 20
 
10.0%

Length

2023-12-27T13:04:29.501538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:29.933220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
gas 181
90.0%
diesel 20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 663
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 663
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiration
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
165 
turbo
36 

Length

Max length5
Median length3
Mean length3.358209
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 165
82.1%
turbo 36
 
17.9%

Length

2023-12-27T13:04:30.340787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:30.754173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
std 165
82.1%
turbo 36
 
17.9%

Most occurring characters

ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

num-of-doors
Categorical

Distinct2
Distinct (%)1.0%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
four
113 
two
86 

Length

Max length4
Median length4
Mean length3.5678392
Min length3

Characters and Unicode

Total characters710
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 113
56.2%
two 86
42.8%
(Missing) 2
 
1.0%

Length

2023-12-27T13:04:31.151525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:31.483381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
four 113
56.8%
two 86
43.2%

Most occurring characters

ValueCountFrequency (%)
o 199
28.0%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 710
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 199
28.0%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 710
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 199
28.0%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 199
28.0%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%

body-style
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
94 
hatchback
68 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6119403
Min length5

Characters and Unicode

Total characters1329
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 94
46.8%
hatchback 68
33.8%
wagon 25
 
12.4%
hardtop 8
 
4.0%
convertible 6
 
3.0%

Length

2023-12-27T13:04:31.880551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:32.231713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
sedan 94
46.8%
hatchback 68
33.8%
wagon 25
 
12.4%
hardtop 8
 
4.0%
convertible 6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1329
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1329
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

drive-wheels
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
118 
rwd
75 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 118
58.7%
rwd 75
37.3%
4wd 8
 
4.0%

Length

2023-12-27T13:04:32.664258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:33.056242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
fwd 118
58.7%
rwd 75
37.3%
4wd 8
 
4.0%

Most occurring characters

ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 595
98.7%
Decimal Number 8
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 201
33.8%
d 201
33.8%
f 118
19.8%
r 75
 
12.6%
Decimal Number
ValueCountFrequency (%)
4 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 595
98.7%
Common 8
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 201
33.8%
d 201
33.8%
f 118
19.8%
r 75
 
12.6%
Common
ValueCountFrequency (%)
4 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

engine-location
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
198 
rear
 
3

Length

Max length5
Median length5
Mean length4.9850746
Min length4

Characters and Unicode

Total characters1002
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 198
98.5%
rear 3
 
1.5%

Length

2023-12-27T13:04:33.438237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:33.817778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
front 198
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1002
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1002
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

Distinct52
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.797015
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:34.228512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0663656
Coefficient of variation (CV)0.061402316
Kurtosis0.9484451
Mean98.797015
Median Absolute Deviation (MAD)2.8
Skewness1.0312614
Sum19858.2
Variance36.800791
MonotonicityNot monotonic
2023-12-27T13:04:34.772060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.7 20
 
10.0%
94.5 19
 
9.5%
95.7 13
 
6.5%
96.5 8
 
4.0%
97.3 7
 
3.5%
100.4 6
 
3.0%
107.9 6
 
3.0%
99.1 6
 
3.0%
98.8 6
 
3.0%
98.4 6
 
3.0%
Other values (42) 104
51.7%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.5%
93.3 1
 
0.5%
93.7 20
10.0%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.5%
108 1
 
0.5%
107.9 6
3.0%
106.7 1
 
0.5%

length
Real number (ℝ)

Distinct73
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.201
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:35.237793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.8
median173.2
Q3183.5
95-th percentile197
Maximum208.1
Range67
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation12.322175
Coefficient of variation (CV)0.070735389
Kurtosis-0.065191628
Mean174.201
Median Absolute Deviation (MAD)6.9
Skewness0.15444635
Sum35014.4
Variance151.836
MonotonicityNot monotonic
2023-12-27T13:04:35.705369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.5%
188.8 11
 
5.5%
171.7 7
 
3.5%
186.7 7
 
3.5%
166.3 7
 
3.5%
165.3 6
 
3.0%
177.8 6
 
3.0%
176.2 6
 
3.0%
186.6 6
 
3.0%
172 5
 
2.5%
Other values (63) 125
62.2%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 1
 
0.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.5%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

width
Real number (ℝ)

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.889055
Minimum60.3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:36.477876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.6
95-th percentile70.3
Maximum72
Range11.7
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.1014708
Coefficient of variation (CV)0.031894081
Kurtosis0.67865517
Mean65.889055
Median Absolute Deviation (MAD)1.4
Skewness0.87502904
Sum13243.7
Variance4.4161796
MonotonicityNot monotonic
2023-12-27T13:04:37.101448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
63.8 24
 
11.9%
66.5 23
 
11.4%
65.4 15
 
7.5%
68.4 10
 
5.0%
64.4 10
 
5.0%
63.6 9
 
4.5%
64 9
 
4.5%
65.5 8
 
4.0%
65.2 7
 
3.5%
65.6 6
 
3.0%
Other values (33) 80
39.8%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 9
 
4.5%
63.8 24
11.9%
63.9 3
 
1.5%
64 9
 
4.5%
64.1 2
 
1.0%
64.2 6
 
3.0%
ValueCountFrequency (%)
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%
68.8 1
 
0.5%

height
Real number (ℝ)

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.766667
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:37.693994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.4478222
Coefficient of variation (CV)0.045526761
Kurtosis-0.43290815
Mean53.766667
Median Absolute Deviation (MAD)1.6
Skewness0.029173299
Sum10807.1
Variance5.9918333
MonotonicityNot monotonic
2023-12-27T13:04:38.253874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
7.0%
55.7 12
 
6.0%
54.1 10
 
5.0%
54.5 10
 
5.0%
55.5 9
 
4.5%
52 9
 
4.5%
56.7 8
 
4.0%
54.3 8
 
4.0%
52.6 7
 
3.5%
56.1 7
 
3.5%
Other values (39) 107
53.2%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
3.0%
50.5 1
 
0.5%
50.6 5
 
2.5%
50.8 14
7.0%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
4.0%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.5%

curb-weight
Real number (ℝ)

Distinct169
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.6667
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:38.808634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1905
Q12169
median2414
Q32926
95-th percentile3505
Maximum4066
Range2578
Interquartile range (IQR)757

Descriptive statistics

Standard deviation517.29673
Coefficient of variation (CV)0.20241166
Kurtosis0.034915576
Mean2555.6667
Median Absolute Deviation (MAD)377
Skewness0.70580359
Sum513689
Variance267595.9
MonotonicityNot monotonic
2023-12-27T13:04:39.322280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2579 2
 
1.0%
2403 2
 
1.0%
2290 2
 
1.0%
2145 2
 
1.0%
2756 2
 
1.0%
3139 2
 
1.0%
Other values (159) 176
87.6%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 1
0.5%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

engine-type
Categorical

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
145 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12

Length

Max length5
Median length3
Mean length3.119403
Min length1

Characters and Unicode

Total characters627
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 145
72.1%
ohcf 15
 
7.5%
ohcv 13
 
6.5%
dohc 12
 
6.0%
l 12
 
6.0%
rotor 4
 
2.0%

Length

2023-12-27T13:04:39.861619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:40.301256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ohc 145
72.1%
ohcf 15
 
7.5%
ohcv 13
 
6.5%
dohc 12
 
6.0%
l 12
 
6.0%
rotor 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 627
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 627
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

num-of-cylinders
Categorical

IMBALANCE 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
157 
six
24 
five
 
10
two
 
4
eight
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.8955224
Min length3

Characters and Unicode

Total characters783
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 157
78.1%
six 24
 
11.9%
five 10
 
5.0%
two 4
 
2.0%
eight 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-12-27T13:04:40.760222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:41.209116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
four 157
78.1%
six 24
 
11.9%
five 10
 
5.0%
two 4
 
2.0%
eight 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 783
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 783
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

engine-size
Real number (ℝ)

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.87562
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:41.671797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3141
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)43

Descriptive statistics

Standard deviation41.546834
Coefficient of variation (CV)0.32746113
Kurtosis5.4974908
Mean126.87562
Median Absolute Deviation (MAD)22
Skewness1.9791442
Sum25502
Variance1726.1395
MonotonicityNot monotonic
2023-12-27T13:04:42.204527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
122 15
 
7.5%
92 15
 
7.5%
98 14
 
7.0%
97 14
 
7.0%
108 13
 
6.5%
110 12
 
6.0%
90 10
 
5.0%
109 8
 
4.0%
141 7
 
3.5%
120 7
 
3.5%
Other values (33) 86
42.8%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 10
5.0%
91 5
 
2.5%
92 15
7.5%
97 14
7.0%
98 14
7.0%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
194 3
1.5%
183 4
2.0%
181 6
3.0%
173 1
 
0.5%

fuel-system
Categorical

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
92 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8955224
Min length3

Characters and Unicode

Total characters783
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 92
45.8%
2bbl 64
31.8%
idi 20
 
10.0%
1bbl 11
 
5.5%
spdi 9
 
4.5%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-12-27T13:04:42.659115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-27T13:04:43.121936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 92
45.8%
2bbl 64
31.8%
idi 20
 
10.0%
1bbl 11
 
5.5%
spdi 9
 
4.5%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 705
90.0%
Decimal Number 78
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 156
22.1%
i 143
20.3%
p 102
14.5%
f 94
13.3%
m 93
13.2%
l 78
11.1%
d 29
 
4.1%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 705
90.0%
Common 78
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 156
22.1%
i 143
20.3%
p 102
14.5%
f 94
13.3%
m 93
13.2%
l 78
11.1%
d 29
 
4.1%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

bore
Real number (ℝ)

MISSING 

Distinct38
Distinct (%)19.3%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.3307107
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:43.665837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.96
Q13.15
median3.31
Q33.59
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.27079343
Coefficient of variation (CV)0.081301999
Kurtosis-0.84272087
Mean3.3307107
Median Absolute Deviation (MAD)0.26
Skewness-0.032621687
Sum656.15
Variance0.073329084
MonotonicityNot monotonic
2023-12-27T13:04:44.141230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.4%
3.19 20
 
10.0%
3.15 15
 
7.5%
2.97 12
 
6.0%
3.03 10
 
5.0%
3.46 9
 
4.5%
3.31 8
 
4.0%
3.78 8
 
4.0%
3.43 8
 
4.0%
3.27 7
 
3.5%
Other values (28) 77
38.3%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.5%
2.92 1
 
0.5%
2.97 12
6.0%
2.99 1
 
0.5%
3.01 5
2.5%
3.03 10
5.0%
3.05 6
3.0%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 1
 
0.5%
3.8 2
 
1.0%
3.78 8
 
4.0%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.5%
3.63 2
 
1.0%
3.62 23
11.4%
3.61 1
 
0.5%
3.6 1
 
0.5%

stroke
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)18.3%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.2569036
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:44.604349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31925624
Coefficient of variation (CV)0.098024469
Kurtosis2.0287842
Mean3.2569036
Median Absolute Deviation (MAD)0.17
Skewness-0.69377839
Sum641.61
Variance0.10192455
MonotonicityNot monotonic
2023-12-27T13:04:45.052356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 19
 
9.5%
3.23 14
 
7.0%
3.15 14
 
7.0%
3.03 14
 
7.0%
3.39 13
 
6.5%
2.64 11
 
5.5%
3.29 9
 
4.5%
3.35 9
 
4.5%
3.46 8
 
4.0%
3.27 6
 
3.0%
Other values (26) 80
39.8%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.5%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
7.0%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.5%
3.58 6
3.0%
3.54 4
2.0%
3.52 5
2.5%
3.5 6
3.0%
3.47 4
2.0%
3.46 8
4.0%

compression-ratio
Real number (ℝ)

Distinct32
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.164279
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:45.492771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.9
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.0049655
Coefficient of variation (CV)0.39402358
Kurtosis5.0688725
Mean10.164279
Median Absolute Deviation (MAD)0.4
Skewness2.5844624
Sum2043.02
Variance16.039749
MonotonicityNot monotonic
2023-12-27T13:04:45.905190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.9%
9.4 26
12.9%
8.5 14
 
7.0%
9.5 13
 
6.5%
9.3 11
 
5.5%
8.7 9
 
4.5%
8 8
 
4.0%
9.2 8
 
4.0%
7 6
 
3.0%
8.6 5
 
2.5%
Other values (22) 55
27.4%
ValueCountFrequency (%)
7 6
3.0%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
4.0%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
7.0%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 2
 
1.0%

horsepower
Real number (ℝ)

Distinct58
Distinct (%)29.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean103.39698
Minimum48
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:46.429951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile176.6
Maximum262
Range214
Interquartile range (IQR)46

Descriptive statistics

Standard deviation37.553843
Coefficient of variation (CV)0.36320056
Kurtosis1.2786708
Mean103.39698
Median Absolute Deviation (MAD)25
Skewness1.1415843
Sum20576
Variance1410.2911
MonotonicityNot monotonic
2023-12-27T13:04:46.919479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.5%
69 10
 
5.0%
70 9
 
4.5%
116 9
 
4.5%
110 8
 
4.0%
95 7
 
3.5%
88 6
 
3.0%
62 6
 
3.0%
101 6
 
3.0%
114 6
 
3.0%
Other values (48) 113
56.2%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.0%
64 1
 
0.5%
68 19
9.5%
69 10
5.0%
ValueCountFrequency (%)
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
 
1.0%
182 3
1.5%
176 2
 
1.0%
175 1
 
0.5%
162 2
 
1.0%
161 2
 
1.0%
160 5
2.5%

peak-rpm
Real number (ℝ)

Distinct22
Distinct (%)11.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean5117.5879
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:47.315260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4245
Q14800
median5200
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation480.52182
Coefficient of variation (CV)0.093896154
Kurtosis0.076587832
Mean5117.5879
Median Absolute Deviation (MAD)300
Skewness0.10772929
Sum1018400
Variance230901.22
MonotonicityNot monotonic
2023-12-27T13:04:47.717654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5500 36
17.9%
4800 36
17.9%
5000 27
13.4%
5200 23
11.4%
5400 11
 
5.5%
6000 9
 
4.5%
5800 7
 
3.5%
5250 7
 
3.5%
4500 7
 
3.5%
4150 5
 
2.5%
Other values (12) 31
15.4%
ValueCountFrequency (%)
4150 5
 
2.5%
4200 5
 
2.5%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.5%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.9%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.5%
5900 3
 
1.5%
5800 7
 
3.5%
5600 1
 
0.5%
5500 36
17.9%
5400 11
 
5.5%
5300 1
 
0.5%
5250 7
 
3.5%
5200 23
11.4%

city-mpg
Real number (ℝ)

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.179104
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:48.171201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.4232205
Coefficient of variation (CV)0.25510123
Kurtosis0.75396809
Mean25.179104
Median Absolute Deviation (MAD)5
Skewness0.68043347
Sum5061
Variance41.257761
MonotonicityNot monotonic
2023-12-27T13:04:48.620636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.9%
19 27
13.4%
24 22
10.9%
27 14
 
7.0%
26 12
 
6.0%
17 12
 
6.0%
23 12
 
6.0%
21 8
 
4.0%
25 8
 
4.0%
30 8
 
4.0%
Other values (19) 50
24.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 5
 
2.5%
17 12
6.0%
18 3
 
1.5%
19 27
13.4%
20 3
 
1.5%
21 8
 
4.0%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 5
2.5%
37 6
3.0%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway-mpg
Real number (ℝ)

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.686567
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:49.054507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8151499
Coefficient of variation (CV)0.22208903
Kurtosis0.56117114
Mean30.686567
Median Absolute Deviation (MAD)5
Skewness0.54950715
Sum6168
Variance46.446269
MonotonicityNot monotonic
2023-12-27T13:04:49.509227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.5%
38 17
 
8.5%
24 17
 
8.5%
30 16
 
8.0%
32 16
 
8.0%
34 14
 
7.0%
37 13
 
6.5%
28 12
 
6.0%
29 10
 
5.0%
33 9
 
4.5%
Other values (20) 58
28.9%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 7
 
3.5%
23 7
 
3.5%
24 17
8.5%
25 19
9.5%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 2
 
1.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.5%

price
Real number (ℝ)

Distinct186
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13207.129
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-27T13:04:50.008981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.0663
Coefficient of variation (CV)0.60172549
Kurtosis3.2315369
Mean13207.129
Median Absolute Deviation (MAD)3306
Skewness1.8096753
Sum2654633
Variance63155863
MonotonicityNot monotonic
2023-12-27T13:04:50.512410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
8845 2
 
1.0%
8495 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
7957 2
 
1.0%
7775 2
 
1.0%
5572 2
 
1.0%
Other values (176) 181
90.0%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2023-12-27T13:04:18.790641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:52.366786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:58.423434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:04.109868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:13.228624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:19.309805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:25.504992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:31.635943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:36.975158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:42.076382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:47.600407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:52.399040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:57.959465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:03.245504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:08.359322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:13.495670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:19.129333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:52.943365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:58.876686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:04.327567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:13.603543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:19.704514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:25.908452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:31.967259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:37.286116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:42.377138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:47.867218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:52.723891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:58.248692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:03.530403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:08.645380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:13.833999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:19.448681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:53.393337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:59.225388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:04.901037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:14.002863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:20.062486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:26.254385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:32.307880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:37.619361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:42.733057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:48.183280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:53.064376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:58.604638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:03.866742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:08.903626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:14.178185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:19.894078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:53.765461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:59.557144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:05.444912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:14.391021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:20.476127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:26.760667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:32.745471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:37.954011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:43.034613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:48.517215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:53.415787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:58.909606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:04.454719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:09.235472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:14.496072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:20.209368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:54.073115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:59.993501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:06.342517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:14.800929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:20.884560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:27.137432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:33.120844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:38.284135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:43.368705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:48.813292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:53.779331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:59.231576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:04.762451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:09.569403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:14.840800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:20.594750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:54.292028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:00.397758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:06.911688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:15.183872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:21.242172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:27.777680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:33.447721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:38.602245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:43.716988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:49.101349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:54.144293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:59.552986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:05.086789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:09.912073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:15.153231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:20.983219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:54.551621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:00.845218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:07.728275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:15.563960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:21.539107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:28.145314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:33.751648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:38.931918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:43.992674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:49.386914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:54.497352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:59.865327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:05.387633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:10.245531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:15.463771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:21.559031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:54.867014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:01.191371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:08.176474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:15.942699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:21.822587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:28.458859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:34.065020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:39.235478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:44.351004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:49.656591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:54.831913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:00.179501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:05.669646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:10.546116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:15.811289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:21.879240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:55.212130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:01.642105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:08.698464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:16.328908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:22.132532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:28.783835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:34.382840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:39.519888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:44.685906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:49.999275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:55.123248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:00.468945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:05.963307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:10.850235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:16.147507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:22.240173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:55.580689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:02.008151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:09.866096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:16.720120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:22.455033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:29.149819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:34.738564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:39.820333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:45.011609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:50.280639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:55.563988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:00.815301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:06.287036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:11.196950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:16.478538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:22.578909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:55.978485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:02.290061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:10.209636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:17.046331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:23.242427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:29.411237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:35.035647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:40.118408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:45.346452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:50.545364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:55.848470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:01.064151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:06.562850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:11.478798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:16.802488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:22.909344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:56.312858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:02.657212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:10.962041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:17.486947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:23.584025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:29.770089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:35.367444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:40.418738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:45.942296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:50.865798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:56.232393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:01.358777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:06.873689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:11.827946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:17.129287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:23.244693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:56.595974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:02.930847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:11.293761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:17.811113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:23.919328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:30.099636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:35.652018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:40.720314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:46.230477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:51.165751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:56.597315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:01.657279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:07.155531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:12.129389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:17.445984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:23.593547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:56.940998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:03.178153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:11.689772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:18.122028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:24.270717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:30.419866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:35.920213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:41.033445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:46.504813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:51.431721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:56.902101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:01.955326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:07.420030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:12.440102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:17.736987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:23.959262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:57.396779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:03.410332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:12.087204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:18.554489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:24.704613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:30.724407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:36.252672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:41.382214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:46.869187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:51.749889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:57.278120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:02.299493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:07.706777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:12.773511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:18.073009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:24.295540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:02:57.911898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:03.734995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:12.756163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:18.947794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:25.119175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:31.222944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:36.603677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:41.700550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:47.234092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:52.064098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:03:57.615058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:02.630582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:08.036205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:13.145180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-27T13:04:18.408211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2023-12-27T13:04:24.914003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-27T13:04:26.228013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212713495
13NaNalfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212716500
21NaNalfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500
32164.0audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950
42164.0audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.0115.05500.0182217450
52NaNaudigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250
61158.0audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.5110.05500.0192517710
71NaNaudigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.5110.05500.0192518920
81158.0audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.3140.05500.0172023875
92192.0bmwgasstdtwosedanrwdfront101.2176.864.854.32395ohcfour108mpfi3.502.808.8101.05800.0232916430
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
191-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.5114.05400.0232813415
192-2103.0volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.5114.05400.0242815985
193-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.5114.05400.0242816515
194-2103.0volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.5162.05100.0172218420
195-174.0volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.5162.05100.0172218950
196-195.0volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.5114.05400.0232816845
197-195.0volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.7160.05300.0192519045
198-195.0volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485
199-195.0volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.0106.04800.0262722470
200-195.0volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.5114.05400.0192522625